An organization may place its data into a centralized database from which complex decision-support queries can be made. These queries typically need to process and combine large amounts of data very efficiently. Answers to these queries can be very valuable in the application domain, enabling informed changes to organizational behavior. New techniques for evaluating and optimizing general decision-support queries are being developed in the context of a main-memory database. It is becoming possible to store gigabytes of data in RAM. Thus one begins to ask whether decision support query processing could obtain the performance benefits of having data reside in main memory. The approach taken is to study the performance issues associated with processing complex queries in main memory. By designing and using algorithms, access structures, and data representations tailored to main-memory databases, we can avoid much of the overhead that is apparent in a disk-based system even when the data set fits in main-memory. By allowing updates only in an off-line batch window, one gains the flexibility to organize the data in a way that is tuned for fast query response. A prototype system is being implemented to demonstrate the benefits of the proposed techniques, measured in terms of query response times. The system has a Web interface. The prototype will be evaluated using medical data from the Columbia Presbyterian Medical Center. The novel techniques developed in the course of this project will have broad use in applications that process complex queries over datasets that can fit in main memory. www.cs.columbia.edu/~kar
